Evolution of the semiconductor manufacturing industry is placing greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions continue to shrink, yet the industry needs to decrease time for achieving high-yield, high-value production. Minimizing the total time from detecting a yield problem to fixing it determines the return-on-investment for a semiconductor manufacturer.
Fabricating semiconductor devices, such as logic and memory devices, typically includes processing a semiconductor wafer using a large number of fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a photoresist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Metrology may be used during semiconductor manufacturing to take various measurements of, for example, a semiconductor wafer or reticle. Metrology tools can be used to measure structural and material characteristics associated with various semiconductor fabrication processes. For example, the metrology tools can measure material composition or can measure dimensional characteristics of structures and films such as film thickness, critical dimension (CD) of structures, or overlay. These measurements are used to facilitate process controls and/or yield efficiencies during the manufacture of semiconductor dies.
As semiconductor device pattern dimensions continue to shrink, smaller metrology targets are often required. Furthermore, the requirements for measurement accuracy and matching to actual device characteristics increase the need for device-like targets as well as in-die and even on-device measurements. Various metrology implementations have been proposed to achieve that goal. For example, focused beam ellipsometry based on primarily reflective optics has been proposed. Apodizers can be used to mitigate the effects of optical diffraction causing the spread of the illumination spot beyond the size defined by geometric optics. The use of high-numerical-aperture tools with simultaneous multiple angle-of-incidence illumination is another way to achieve small-target capability.
Other measurement examples may include measuring the composition of one or more layers of the semiconductor stack, measuring certain defects on (or within) the wafer, and measuring the amount of photolithographic radiation exposed to the wafer. In some cases, a metrology tool and algorithm may be configured for measuring non-periodic targets.
Measurement of parameters of interest usually involves a number of algorithms. For example, optical interaction of the incident beam with the sample is modeled using electromagnetic (EM) solver and uses such algorithms as rigorous coupled-wave analysis (RCWA), finite element method (FEM), method of moments, surface integral method, volume integral method, finite-difference time-domain (FDTD), and others. The target of interest is usually modeled (parametrized) using a geometric engine, or in some cases, process modeling engine or a combination of both. A geometric engine is implemented in these cases.
Collected data can be analyzed by a number of data fitting and optimization techniques an technologies including libraries; Fast-reduced-order models; regression; machine-learning algorithms such as neural networks, support-vector machines (SVM); dimensionality-reduction algorithms such as, e.g., principal component analysis (PCA), independent component analysis (ICA), and local-linear embedding (LLE); sparse representation such as Fourier or wavelet transform; Kalman filters; algorithms to promote matching from same or different tool types; and others.
Collected data can also be analyzed by algorithms that do not include modeling, optimization and/or fitting.
Computational algorithms are usually optimized for metrology applications with one or more approaches being used such as design and implementation of computational hardware, parallelization, distribution of computation, load-balancing, multi-service support, dynamic load optimization, etc. Different implementations of algorithms can be done in firmware, software, field-programmable gate array (FPGA), programmable optics components, etc.
The data analysis and fitting steps usually pursue one or more of: (1) measurement of CD, sidewall angle (SWA), shape, stress, composition, films, bandgap, electrical properties, focus/dose, overlay, generating process parameters (e.g., resist state, partial pressure, temperature, focusing model), and/or any combination thereof; (2) modeling and/or design of metrology systems; and (3) modeling, design, and/or optimization of metrology targets.
In the presently available film measurement systems, an illuminating beam of light passes first through the film stack to be measured and then through a grating or prism. An image of the resulting spectrum is produced on a sensor comprising an array of pixels, which are digitized and conveyed to a computing engine. The computing engine uses modeling techniques to determine the properties of the film stack, such as the thickness or material properties of each layer.
One problem these systems exhibit is that their spectral resolutions are limited by their optical point spread functions (PSFs) and sensor pixel sizes. The problem is aggravated when measuring thick film stacks, such as high aspect ratio (HAR) devices, for example, 3D-Flash, when the width of the PSF is larger than the period of the spectral signal. Such a signal is then further attenuated as it is quantized into individual pixels that are also of a similar order to the period of the spectral signal.
These attenuating effects are typically deconvolved from the signal prior to solving for film stack parameters, as modelling these effects is costly in computing resources. However, deconvolution fails to correctly reconstruct the ideal spectrum with thick film stacks. This failure is most evident in the shorter wavelengths like ultraviolet.
Additionally, thick film stacks produce high frequency responses for shorter wavelengths. Conventional techniques are unable to reconstruct the ideal spectrum for post-processing. Computing engines using the conventional techniques cannot correctly determine the underlying film stack properties. This greatly diminishes the effectiveness of the inspection tool.
The present disclosure overcomes these and other limitations, thus improving the ability of an inspection tool to measure new types of film stacks.